wkNNMI: wkNNMI: An Adaptive Mutual Information-Weighted k-NN...
In wkNNMI: A Mutual Information-Weighted k-NN Imputation Algorithm

This package implements an adaptive weighted k-nearest neighbours (wk-NN) imputation algorithm for clinical register data developed to explicitly handle missing values of continuous/ordinal/categorical and static/dynamic features conjointly. For each subject with missing data to be imputed, the method creates a feature vector constituted by the information collected over his/her first *window_size* time units of visits. This vector is used as sample in a k-nearest neighbours procedure, in order to select, among the other patients, the ones with the most similar temporal evolution of the disease over time. An *ad hoc* similarity metric was implemented for the sample comparison, capable of handling the different nature of the data, the presence of multiple missing values and include the cross-information among features.

The wkNNMI package mainly serves as container for the two functions that implement the imputation algorithm impute.subject() and impute.wknn(), and for the example datasets patient.data and new.patient.

wkNNMI documentation built on March 26, 2020, 6:26 p.m.